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Chen JS, Chow RD, Song E, Mao T, Israelow B, Kamath K, Bozekowski J, Haynes WA, Filler RB, Menasche BL, Wei J, Alfajaro MM, Song W, Peng L, Carter L, Weinstein JS, Gowthaman U, Chen S, Craft J, Shon JC, Iwasaki A, Wilen CB, Eisenbarth SC. High-affinity, neutralizing antibodies to SARS-CoV-2 can be made without T follicular helper cells. Sci Immunol 2022; 7:eabl5652. [PMID: 34914544 PMCID: PMC8977051 DOI: 10.1126/sciimmunol.abl5652] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
T follicular helper (TFH) cells are the conventional drivers of protective, germinal center (GC)–based antiviral antibody responses. However, loss of TFH cells and GCs has been observed in patients with severe COVID-19. As T cell–B cell interactions and immunoglobulin class switching still occur in these patients, noncanonical pathways of antibody production may be operative during SARS-CoV-2 infection. We found that both TFH-dependent and -independent antibodies were induced against SARS-CoV-2 infection, SARS-CoV-2 vaccination, and influenza A virus infection. Although TFH-independent antibodies to SARS-CoV-2 had evidence of reduced somatic hypermutation, they were still high affinity, durable, and reactive against diverse spike-derived epitopes and were capable of neutralizing both homologous SARS-CoV-2 and the B.1.351 (beta) variant of concern. We found by epitope mapping and B cell receptor sequencing that TFH cells focused the B cell response, and therefore, in the absence of TFH cells, a more diverse clonal repertoire was maintained. These data support an alternative pathway for the induction of B cell responses during viral infection that enables effective, neutralizing antibody production to complement traditional GC-derived antibodies that might compensate for GCs damaged by viral inflammation.
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Affiliation(s)
- Jennifer S. Chen
- Department of Laboratory Medicine, Yale University School of Medicine; New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Ryan D. Chow
- Department of Genetics, Yale University School of Medicine; New Haven, CT, USA
- Systems Biology Institute, Yale University; West Haven, CT, USA
| | - Eric Song
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Tianyang Mao
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Benjamin Israelow
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
- Department of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine; New Haven, CT, USA
| | | | | | | | - Renata B. Filler
- Department of Laboratory Medicine, Yale University School of Medicine; New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Bridget L. Menasche
- Department of Laboratory Medicine, Yale University School of Medicine; New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Jin Wei
- Department of Laboratory Medicine, Yale University School of Medicine; New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Mia Madel Alfajaro
- Department of Laboratory Medicine, Yale University School of Medicine; New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Wenzhi Song
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Lei Peng
- Department of Genetics, Yale University School of Medicine; New Haven, CT, USA
- Systems Biology Institute, Yale University; West Haven, CT, USA
| | - Lauren Carter
- Institute for Protein Design, University of Washington; Seattle, WA, USA
| | - Jason S. Weinstein
- Center for Immunity and Inflammation, Rutgers New Jersey Medical School; Newark, NJ, USA
| | - Uthaman Gowthaman
- Deparment of Pathology, University of Massachusetts Medical School; Worcester, MA, USA
| | - Sidi Chen
- Department of Genetics, Yale University School of Medicine; New Haven, CT, USA
- Systems Biology Institute, Yale University; West Haven, CT, USA
| | - Joe Craft
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | | | - Akiko Iwasaki
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
- Howard Hughes Medical Institute; Chevy Chase, MD, USA
| | - Craig B. Wilen
- Department of Laboratory Medicine, Yale University School of Medicine; New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
| | - Stephanie C. Eisenbarth
- Department of Laboratory Medicine, Yale University School of Medicine; New Haven, CT, USA
- Department of Immunobiology, Yale University School of Medicine; New Haven, CT, USA
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2
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Haynes WA, Kamath K, Bozekowski J, Baum-Jones E, Campbell M, Casanovas-Massana A, Daugherty PS, Dela Cruz CS, Dhal A, Farhadian SF, Fitzgibbons L, Fournier J, Jhatro M, Jordan G, Klein J, Lucas C, Kessler D, Luchsinger LL, Martinez B, Catherine Muenker M, Pischel L, Reifert J, Sawyer JR, Waitz R, Wunder EA, Zhang M, Iwasaki A, Ko A, Shon JC. High-resolution epitope mapping and characterization of SARS-CoV-2 antibodies in large cohorts of subjects with COVID-19. Commun Biol 2021; 4:1317. [PMID: 34811480 PMCID: PMC8608966 DOI: 10.1038/s42003-021-02835-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 10/22/2021] [Indexed: 12/12/2022] Open
Abstract
As Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to spread, characterization of its antibody epitopes, emerging strains, related coronaviruses, and even the human proteome in naturally infected patients can guide the development of effective vaccines and therapies. Since traditional epitope identification tools are dependent upon pre-defined peptide sequences, they are not readily adaptable to diverse viral proteomes. The Serum Epitope Repertoire Analysis (SERA) platform leverages a high diversity random bacterial display library to identify proteome-independent epitope binding specificities which are then analyzed in the context of organisms of interest. When evaluating immune response in the context of SARS-CoV-2, we identify dominant epitope regions and motifs which demonstrate potential to classify mild from severe disease and relate to neutralization activity. We highlight SARS-CoV-2 epitopes that are cross-reactive with other coronaviruses and demonstrate decreased epitope signal for mutant SARS-CoV-2 strains. Collectively, the evolution of SARS-CoV-2 mutants towards reduced antibody response highlight the importance of data-driven development of the vaccines and therapies to treat COVID-19. Using a high throughput, random bacterial peptide display approach applied to patient serum samples, Haynes, Kamath, Bozekowski et al identify the antigens and epitopes that elicit a SARS-CoV-2 humoral response. They identify differences depending on disease severity and further in silico analysis suggests decreased epitope signal for Q677P but not for D614G mutant SARSCoV-2 strains.
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Affiliation(s)
| | | | | | | | - Melissa Campbell
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Arnau Casanovas-Massana
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | | | - Charles S Dela Cruz
- Department of Medicine, Section of Pulmonary and Critical Care Medicine, Yale University School of Medicine, New Haven, CT, USA
| | | | - Shelli F Farhadian
- Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, USA
| | | | - John Fournier
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | | | | | - Jon Klein
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Carolina Lucas
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | | | | | | | - M Catherine Muenker
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | - Lauren Pischel
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.,Department of Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, USA
| | | | | | | | - Elsio A Wunder
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
| | | | | | - Akiko Iwasaki
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.,Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Albert Ko
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
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Wisnewski AV, Redlich CA, Liu J, Kamath K, Abad QA, Smith RF, Fazen L, Santiago R, Campillo Luna J, Martinez B, Baum-Jones E, Waitz R, Haynes WA, Shon JC. Immunogenic amino acid motifs and linear epitopes of COVID-19 mRNA vaccines. PLoS One 2021; 16:e0252849. [PMID: 34499652 PMCID: PMC8428655 DOI: 10.1371/journal.pone.0252849] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 07/18/2021] [Indexed: 12/12/2022] Open
Abstract
Reverse vaccinology is an evolving approach for improving vaccine effectiveness and minimizing adverse responses by limiting immunizations to critical epitopes. Towards this goal, we sought to identify immunogenic amino acid motifs and linear epitopes of the SARS-CoV-2 spike protein that elicit IgG in COVID-19 mRNA vaccine recipients. Paired pre/post vaccination samples from N = 20 healthy adults, and post-vaccine samples from an additional N = 13 individuals were used to immunoprecipitate IgG targets expressed by a bacterial display random peptide library, and preferentially recognized peptides were mapped to the spike primary sequence. The data identify several distinct amino acid motifs recognized by vaccine-induced IgG, a subset of those targeted by IgG from natural infection, which may mimic 3-dimensional conformation (mimotopes). Dominant linear epitopes were identified in the C-terminal domains of the S1 and S2 subunits (aa 558-569, 627-638, and 1148-1159) which have been previously associated with SARS-CoV-2 neutralization in vitro and demonstrate identity to bat coronavirus and SARS-CoV, but limited homology to non-pathogenic human coronavirus. The identified COVID-19 mRNA vaccine epitopes should be considered in the context of variants, immune escape and vaccine and therapy design moving forward.
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Affiliation(s)
- Adam V. Wisnewski
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Carrie A. Redlich
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Jian Liu
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Kathy Kamath
- Serimmune, Inc., Goleta, CA, United States of America
| | - Queenie-Ann Abad
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Richard F. Smith
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Louis Fazen
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Romero Santiago
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | - Julian Campillo Luna
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States of America
| | | | | | - Rebecca Waitz
- Serimmune, Inc., Goleta, CA, United States of America
| | | | - John C. Shon
- Serimmune, Inc., Goleta, CA, United States of America
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4
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Au L, Fendler A, Shepherd STC, Rzeniewicz K, Cerrone M, Byrne F, Carlyle E, Edmonds K, Del Rosario L, Shon J, Haynes WA, Ward B, Shum B, Gordon W, Gerard CL, Xie W, Joharatnam-Hogan N, Young K, Pickering L, Furness AJS, Larkin J, Harvey R, Kassiotis G, Gandhi S, Swanton C, Fribbens C, Wilkinson KA, Wilkinson RJ, Lau DK, Banerjee S, Starling N, Chau I, Turajlic S. Cytokine release syndrome in a patient with colorectal cancer after vaccination with BNT162b2. Nat Med 2021; 27:1362-1366. [PMID: 34040262 PMCID: PMC8363501 DOI: 10.1038/s41591-021-01387-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 05/07/2021] [Indexed: 02/07/2023]
Abstract
Patients with cancer are currently prioritized in coronavirus disease 2019 (COVID-19) vaccination programs globally, which includes administration of mRNA vaccines. Cytokine release syndrome (CRS) has not been reported with mRNA vaccines and is an extremely rare immune-related adverse event of immune checkpoint inhibitors. We present a case of CRS that occurred 5 d after vaccination with BTN162b2 (tozinameran)-the Pfizer-BioNTech mRNA COVID-19 vaccine-in a patient with colorectal cancer on long-standing anti-PD-1 monotherapy. The CRS was evidenced by raised inflammatory markers, thrombocytopenia, elevated cytokine levels (IFN-γ/IL-2R/IL-18/IL-16/IL-10) and steroid responsiveness. The close temporal association of vaccination and diagnosis of CRS in this case suggests that CRS was a vaccine-related adverse event; with anti-PD1 blockade as a potential contributor. Overall, further prospective pharmacovigillence data are needed in patients with cancer, but the benefit-risk profile remains strongly in favor of COVID-19 vaccination in this population.
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Affiliation(s)
- Lewis Au
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - Annika Fendler
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - Scott T C Shepherd
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | | | - Maddalena Cerrone
- Tuberculosis Laboratory, The Francis Crick Institute, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - Fiona Byrne
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - Eleanor Carlyle
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - Kim Edmonds
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - Lyra Del Rosario
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | | | | | - Barry Ward
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - Ben Shum
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - William Gordon
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | - Camille L Gerard
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
- Precision Oncology Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Wenyi Xie
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK
| | | | - Kate Young
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - Lisa Pickering
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - Andrew J S Furness
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - James Larkin
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK
| | - Ruth Harvey
- Worldwide Influenza Centre, The Francis Crick Institute, London, UK
| | - George Kassiotis
- Retroviral Immunology Laboratory, The Francis Crick Institute, London, UK
| | - Sonia Gandhi
- Neurodegeneration Biology Laboratory, The Francis Crick Institute, London, UK
- UCL Queen Square Institute of Neurology, Queen Square, London, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Charlotte Fribbens
- Acute Oncology Service, The Royal Marsden NHS Foundation Trust, London, UK
- Gastrointestinal and Lymphoma Unit, The Royal Marsden NHS Foundation Trust, Sutton, UK
| | | | - Robert J Wilkinson
- Tuberculosis Laboratory, The Francis Crick Institute, London, UK
- Department of Infectious Disease, Imperial College London, London, UK
| | - David K Lau
- Gastrointestinal and Lymphoma Unit, The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Susana Banerjee
- Gynaecology Unit, The Royal Marsden NHS Foundation Trust and Institute of Cancer Research, London, UK
| | - Naureen Starling
- Gastrointestinal and Lymphoma Unit, The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Ian Chau
- Gastrointestinal and Lymphoma Unit, The Royal Marsden NHS Foundation Trust, Sutton, UK
| | - Samra Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London, UK.
- Skin and Renal Units, The Royal Marsden NHS Foundation Trust, London, UK.
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5
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Haynes WA, Kamath K, Waitz R, Daugherty PS, Shon JC. Protein-Based Immunome Wide Association Studies (PIWAS) for the Discovery of Significant Disease-Associated Antigens. Front Immunol 2021; 12:625311. [PMID: 33986742 PMCID: PMC8110919 DOI: 10.3389/fimmu.2021.625311] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 04/07/2021] [Indexed: 12/17/2022] Open
Abstract
Identification of the antigens associated with antibodies is vital to understanding immune responses in the context of infection, autoimmunity, and cancer. Discovering antigens at a proteome scale could enable broader identification of antigens that are responsible for generating an immune response or driving a disease state. Although targeted tests for known antigens can be straightforward, discovering antigens at a proteome scale using protein and peptide arrays is time consuming and expensive. We leverage Serum Epitope Repertoire Analysis (SERA), an assay based on a random bacterial display peptide library coupled with next generation sequencing (NGS), to power the development of Protein-based Immunome Wide Association Study (PIWAS). PIWAS uses proteome-based signals to discover candidate antibody-antigen epitopes that are significantly elevated in a subset of cases compared to controls. After demonstrating statistical power relative to the magnitude and prevalence of effect in synthetic data, we apply PIWAS to systemic lupus erythematosus (SLE, n=31) and observe known autoantigens, Smith and Ribosomal protein P, within the 22 highest scoring candidate protein antigens across the entire human proteome. We validate the magnitude and location of the SLE specific signal against the Smith family of proteins using a cohort of patients who are positive by predicate anti-Sm tests. To test the generalizability of the method in an additional autoimmune disease, we identified and validated autoantigenic signals to SSB, CENPA, and keratin proteins in a cohort of individuals with Sjogren’s syndrome (n=91). Collectively, these results suggest that PIWAS provides a powerful new tool to discover disease-associated serological antigens within any known proteome.
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Affiliation(s)
| | - Kathy Kamath
- Serimmune, Inc., Santa Barbara, CA, United States
| | | | | | - John C Shon
- Serimmune, Inc., Santa Barbara, CA, United States
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Wang K, Dropulic L, Bozekowski J, Pietz HL, Jegaskanda S, Dowdell K, Vogel JS, Garabedian D, Oestreich M, Nguyen H, Ali MA, Lumbard K, Hunsberger S, Reifert J, Haynes WA, Sawyer JR, Shon JC, Daugherty PS, Cohen JI. Serum and Cervicovaginal Fluid Antibody Profiling in Herpes Simplex Virus (HSV) Seronegative Recipients of the HSV529 Vaccine. J Infect Dis 2021; 224:1509-1519. [PMID: 33718970 DOI: 10.1093/infdis/jiab139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 03/12/2021] [Indexed: 11/14/2022] Open
Abstract
Previous HSV2 vaccines have not prevented genital herpes. Concerns have been raised about the choice of antigen, the type of antibody induced by the vaccine, and whether antibody is present in the genital tract where infection occurs. We reported results of a trial of an HSV2 replication-defective vaccine, HSV529, that induced serum neutralizing antibody responses in 78% of HSV1 -/HSV2 - vaccine recipients. Here we show that HSV1 -/HSV2 - vaccine recipients developed antibodies to epitopes of several viral proteins; however, fewer antibody epitopes were detected in vaccine recipients compared with naturally infected persons. HSV529 induced antibodies that mediated HSV2-specific NK cell activation. Depletion of gD-binding antibody from sera reduced neutralizing titers by 62% and NK cell activation by 81%. HSV2 gD antibody was detected in cervicovaginal fluid at about one-third the level of that in serum. A vaccine that induces potent serum antibodies transported to the genital tract might reduce HSV genital infection.
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Affiliation(s)
- Kening Wang
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Lesia Dropulic
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | | | - Harlan L Pietz
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Sinthujan Jegaskanda
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Kennichi Dowdell
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Joshua S Vogel
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Doreen Garabedian
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Makinna Oestreich
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Hanh Nguyen
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Mir A Ali
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Keith Lumbard
- Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Sally Hunsberger
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | | | | | - Jeffrey I Cohen
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
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7
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Chen WS, Haynes WA, Waitz R, Kamath K, Vega-Crespo A, Shrestha R, Zhang M, Foye A, Baselga Carretero I, Perez Garcilazo I, Zhang M, Zhao SG, Sjöström M, Quigley DA, Chou J, Beer TM, Rettig M, Gleave M, Evans CP, Lara P, Chi KN, Reiter RE, Alumkal JJ, Ashworth A, Aggarwal R, Small EJ, Daugherty PS, Ribas A, Oh DY, Shon JC, Feng FY. Autoantibody Landscape in Patients with Advanced Prostate Cancer. Clin Cancer Res 2020; 26:6204-6214. [PMID: 32967941 PMCID: PMC7710628 DOI: 10.1158/1078-0432.ccr-20-1966] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/03/2020] [Accepted: 09/16/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE Autoantibody responses in cancer are of great interest, as they may be concordant with T-cell responses to cancer antigens or predictive of response to cancer immunotherapies. Thus, we sought to characterize the antibody landscape of metastatic castration-resistant prostate cancer (mCRPC). EXPERIMENTAL DESIGN Serum antibody epitope repertoire analysis (SERA) was performed on patient serum to identify tumor-specific neoepitopes. Somatic mutation-specific neoepitopes were investigated by associating serum epitope enrichment scores with whole-genome sequencing results from paired solid tumor metastasis biopsies and germline blood samples. A protein-based immunome-wide association study (PIWAS) was performed to identify significantly enriched epitopes, and candidate serum antibodies enriched in select patients were validated by ELISA profiling. A distinct cohort of patients with melanoma was evaluated to validate the top cancer-specific epitopes. RESULTS SERA was performed on 1,229 serum samples obtained from 72 men with mCRPC and 1,157 healthy control patients. Twenty-nine of 6,636 somatic mutations (0.44%) were associated with an antibody response specific to the mutated peptide. PIWAS analyses identified motifs in 11 proteins, including NY-ESO-1 and HERVK-113, as immunogenic in mCRPC, and ELISA confirmed serum antibody enrichment in candidate patients. Confirmatory PIWAS, Identifying Motifs Using Next-generation sequencing Experiments (IMUNE), and ELISA analyses performed on serum samples from 106 patients with melanoma similarly revealed enriched cancer-specific antibody responses to NY-ESO-1. CONCLUSIONS We present the first large-scale profiling of autoantibodies in advanced prostate cancer, utilizing a new antibody profiling approach to reveal novel cancer-specific antigens and epitopes. Our study recovers antigens of known importance and identifies novel tumor-specific epitopes of translational interest.
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Affiliation(s)
- William S Chen
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | | | | | | | - Agustin Vega-Crespo
- Division of Hematology and Oncology, University of California Los Angeles, Los Angeles, California
| | - Raunak Shrestha
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | | | - Adam Foye
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
- Department of Medicine, University of California San Francisco, San Francisco, California
| | | | - Ivan Perez Garcilazo
- Division of Hematology and Oncology, University of California Los Angeles, Los Angeles, California
| | - Meng Zhang
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | - Shuang G Zhao
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | - Martin Sjöström
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | - David A Quigley
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
- Department of Urology, University of California San Francisco, San Francisco, California
| | - Jonathan Chou
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
- Department of Medicine, University of California San Francisco, San Francisco, California
| | - Tomasz M Beer
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | - Matthew Rettig
- Division of Hematology and Oncology, University of California Los Angeles, Los Angeles, California
- VA Greater Los Angeles Healthcare System, Los Angeles, California
| | - Martin Gleave
- University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Primo Lara
- University of California Davis, Davis, California
| | - Kim N Chi
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert E Reiter
- Department of Urology, University of California Los Angeles, Los Angeles, California
| | - Joshi J Alumkal
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
- Department of Hematology and Oncology, University of Michigan, Ann Arbor, Michigan
| | - Alan Ashworth
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | - Rahul Aggarwal
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
- Division of Hematology and Oncology, University of California Los Angeles, Los Angeles, California
| | - Eric J Small
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
- Department of Medicine, University of California San Francisco, San Francisco, California
| | | | - Antoni Ribas
- Division of Hematology and Oncology, University of California Los Angeles, Los Angeles, California
| | - David Y Oh
- Department of Medicine, University of California San Francisco, San Francisco, California
| | | | - Felix Y Feng
- Department of Radiation Oncology, University of California San Francisco, San Francisco, California.
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
- Department of Urology, University of California San Francisco, San Francisco, California
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Haynes WA, Haddon DJ, Diep VK, Khatri A, Bongen E, Yiu G, Balboni I, Bolen CR, Mao R, Utz PJ, Khatri P. Integrated, multicohort analysis reveals unified signature of systemic lupus erythematosus. JCI Insight 2020; 5:122312. [PMID: 31971918 DOI: 10.1172/jci.insight.122312] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 01/17/2020] [Indexed: 12/27/2022] Open
Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disease that follows an unpredictable disease course and affects multiple organs and tissues. We performed an integrated, multicohort analysis of 7,471 transcriptomic profiles from 40 independent studies to identify robust gene expression changes associated with SLE. We identified a 93-gene signature (SLE MetaSignature) that is differentially expressed in the blood of patients with SLE compared with healthy volunteers; distinguishes SLE from other autoimmune, inflammatory, and infectious diseases; and persists across diverse tissues and cell types. The SLE MetaSignature correlated significantly with disease activity and other clinical measures of inflammation. We prospectively validated the SLE MetaSignature in an independent cohort of pediatric patients with SLE using a microfluidic quantitative PCR (qPCR) array. We found that 14 of the 93 genes in the SLE MetaSignature were independent of IFN-induced and neutrophil-related transcriptional profiles that have previously been associated with SLE. Pathway analysis revealed dysregulation associated with nucleic acid biosynthesis and immunometabolism in SLE. We further refined a neutropoiesis signature and identified underappreciated transcripts related to immune cells and oxidative stress. In our multicohort, transcriptomic analysis has uncovered underappreciated genes and pathways associated with SLE pathogenesis, with the potential to advance clinical diagnosis, biomarker development, and targeted therapeutics for SLE.
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Affiliation(s)
- Winston A Haynes
- Institute for Immunity, Transplantation and Infection.,Division of Biomedical Informatics Research
| | - D James Haddon
- Institute for Immunity, Transplantation and Infection.,Division of Immunology and Rheumatology, Department of Medicine, and
| | - Vivian K Diep
- Institute for Immunity, Transplantation and Infection.,Division of Immunology and Rheumatology, Department of Medicine, and
| | - Avani Khatri
- Institute for Immunity, Transplantation and Infection.,Division of Immunology and Rheumatology, Department of Medicine, and
| | - Erika Bongen
- Institute for Immunity, Transplantation and Infection.,Division of Immunology and Rheumatology, Department of Medicine, and
| | - Gloria Yiu
- Institute for Immunity, Transplantation and Infection.,Division of Immunology and Rheumatology, Department of Medicine, and
| | - Imelda Balboni
- Division of Allergy, Immunology and Rheumatology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | | | - Rong Mao
- Institute for Immunity, Transplantation and Infection.,Division of Immunology and Rheumatology, Department of Medicine, and
| | - Paul J Utz
- Institute for Immunity, Transplantation and Infection.,Division of Immunology and Rheumatology, Department of Medicine, and
| | - Purvesh Khatri
- Institute for Immunity, Transplantation and Infection.,Division of Biomedical Informatics Research
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9
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Abstract
We found tremendous inequality across gene and protein annotation resources. We observed that this bias leads biomedical researchers to focus on richly annotated genes instead of those with the strongest molecular data. We advocate that researchers reduce these biases by pursuing data-driven hypotheses.
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Affiliation(s)
- Winston A Haynes
- Stanford Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, California, USA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, USA
- Biomedical Informatics Training Program, Stanford University, Stanford, California, USA
| | - Aurelie Tomczak
- Stanford Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, California, USA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, USA
| | - Purvesh Khatri
- Stanford Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, California, USA.
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, USA.
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10
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Yiu G, Rasmussen TK, Ajami B, Haddon DJ, Chu AD, Tangsombatvisit S, Haynes WA, Diep V, Steinman L, Faix J, Utz PJ. Development of Th17-Associated Interstitial Kidney Inflammation in Lupus-Prone Mice Lacking the Gene Encoding STAT-1. Arthritis Rheumatol 2017; 68:1233-44. [PMID: 26636548 DOI: 10.1002/art.39535] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 11/24/2015] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Type I interferon (IFN) signaling is a central pathogenic pathway in systemic lupus erythematosus (SLE), and therapeutics targeting type I IFN signaling are in development. Multiple proteins with overlapping functions play a role in IFN signaling, but the signaling events downstream of receptor engagement are unclear. This study was undertaken to investigate the roles of the type I and type II IFN signaling components IFN-α/β/ω receptor 2 (IFNAR-2), IFN regulatory factor 9 (IRF-9), and STAT-1 in a mouse model of SLE. METHODS We used immunohistochemical staining and highly multiplexed assays to characterize pathologic changes in histology, autoantibody production, cytokine/chemokine profiles, and STAT phosphorylation in order to investigate the individual roles of IFNAR-2, IRF-9, and STAT-1 in MRL/lpr mice. RESULTS We found that STAT-1(-/-) mice, but not IRF-9(-/-) or IFNAR-2(-/-) mice, developed interstitial nephritis characterized by infiltration with retinoic acid receptor-related orphan nuclear receptor γt-positive lymphocytes, macrophages, and eosinophils. Despite pronounced interstitial kidney disease and abnormal kidney function, STAT-1(-/-) mice had decreased proteinuria, glomerulonephritis, and autoantibody production. Phosphospecific flow cytometry revealed shunting of STAT phosphorylation from STAT-1 to STAT-3/4. CONCLUSION We describe unique contributions of STAT-1 to pathology in different kidney compartments in a mouse model, and provide potentially novel insight into tubulointerstitial nephritis, a poorly understood complication that predicts end-stage kidney disease in SLE patients.
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Affiliation(s)
- Gloria Yiu
- Stanford University School of Medicine, Stanford, California
| | - Tue K Rasmussen
- Stanford University School of Medicine, Stanford, California, and Aarhus University, Aarhus, Denmark
| | - Bahareh Ajami
- Stanford University School of Medicine, Stanford, California
| | - David J Haddon
- Stanford University School of Medicine, Stanford, California
| | - Alvina D Chu
- Stanford University School of Medicine, Stanford, California
| | | | | | - Vivian Diep
- Stanford University School of Medicine, Stanford, California
| | - Larry Steinman
- Stanford University School of Medicine and Institute for Immunity, Transplantation, and Infection, Stanford, California
| | - James Faix
- Stanford University School of Medicine, Stanford, California
| | - Paul J Utz
- Stanford University School of Medicine and Institute for Immunity, Transplantation, and Infection, Stanford, California
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11
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Haynes WA, Vallania F, Liu C, Bongen E, Tomczak A, Andres-Terrè M, Lofgren S, Tam A, Deisseroth CA, Li MD, Sweeney TE, Khatri P. EMPOWERING MULTI-COHORT GENE EXPRESSION ANALYSIS TO INCREASE REPRODUCIBILITY. Pac Symp Biocomput 2017; 22:144-153. [PMID: 27896970 PMCID: PMC5167529 DOI: 10.1142/9789813207813_0015] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.
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Affiliation(s)
- Winston A Haynes
- Stanford Institute for Immunity, Transplantation, and Infection, Stanford University, USA2Biomedical Informatics Training Program, Stanford University, USA3Stanford Center for Biomedical Informatics Research, Stanford University, USA
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12
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Sweeney TE, Haynes WA, Vallania F, Ioannidis JP, Khatri P. Methods to increase reproducibility in differential gene expression via meta-analysis. Nucleic Acids Res 2016; 45:e1. [PMID: 27634930 PMCID: PMC5224496 DOI: 10.1093/nar/gkw797] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 08/28/2016] [Accepted: 08/31/2016] [Indexed: 12/28/2022] Open
Abstract
Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible. Compared to single-study analysis, gene expression meta-analysis can improve reproducibility by integrating data from multiple studies. However, there are multiple choices in designing and carrying out a meta-analysis. Yet, clear guidelines on best practices are scarce. Here, we hypothesized that studying subsets of very large meta-analyses would allow for systematic identification of best practices to improve reproducibility. We therefore constructed three very large gene expression meta-analyses from clinical samples, and then examined meta-analyses of subsets of the datasets (all combinations of datasets with up to N/2 samples and K/2 datasets) compared to a ‘silver standard’ of differentially expressed genes found in the entire cohort. We tested three random-effects meta-analysis models using this procedure. We showed relatively greater reproducibility with more-stringent effect size thresholds with relaxed significance thresholds; relatively lower reproducibility when imposing extraneous constraints on residual heterogeneity; and an underestimation of actual false positive rate by Benjamini–Hochberg correction. In addition, multivariate regression showed that the accuracy of a meta-analysis increased significantly with more included datasets even when controlling for sample size.
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Affiliation(s)
- Timothy E Sweeney
- Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA.,Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Winston A Haynes
- Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Francesco Vallania
- Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA.,Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - John P Ioannidis
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305, USA.,Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA 94305, USA.,Meta-research Innovation Center at Stanford (METRICS), Stanford, CA 94305, USA
| | - Purvesh Khatri
- Stanford Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA .,Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
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13
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McManus K, Mallory EK, Goldfeder RL, Haynes WA, Tatum JD. Mining Twitter Data to Improve Detection of Schizophrenia. AMIA Jt Summits Transl Sci Proc 2015; 2015:122-6. [PMID: 26306253 PMCID: PMC4525233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Individuals who suffer from schizophrenia comprise I percent of the United States population and are four times more likely to die of suicide than the general US population. Identification of at-risk individuals with schizophrenia is challenging when they do not seek treatment. Microblogging platforms allow users to share their thoughts and emotions with the world in short snippets of text. In this work, we leveraged the large corpus of Twitter posts and machine-learning methodologies to detect individuals with schizophrenia. Using features from tweets such as emoticon use, posting time of day, and dictionary terms, we trained, built, and validated several machine learning models. Our support vector machine model achieved the best performance with 92% precision and 71% recall on the held-out test set. Additionally, we built a web application that dynamically displays summary statistics between cohorts. This enables outreach to undiagnosed individuals, improved physician diagnoses, and destigmatization of schizophrenia.
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